title: Comparison of methods for estimating the effect of salvage therapy in prostate cancer when treatment is given by indication.
abstract: For patients who were previously treated for prostate cancer, salvage hormone therapy is frequently given when the longitudinal marker prostate-specific antigen begins to rise during follow-up. Because the treatment is given by indication, estimating the effect of the hormone therapy is challenging. In a previous paper we described two methods for estimating the treatment effect, called two-stage and sequential stratification. The two-stage method involved modeling the longitudinal and survival data. The sequential stratification method involves contrasts within matched sets of people, where each matched set includes people who did and did not receive hormone therapy. In this paper, we evaluate the properties of these two methods and compare and contrast them with the marginal structural model methodology. The marginal structural model methodology involves a weighted survival analysis, where the weights are derived from models for the time of hormone therapy. We highlight the different conditional and marginal interpretations of the quantities being estimated by the three methods. Using simulations that mimic the prostate cancer setting, we evaluate bias, efficiency, and accuracy of estimated standard errors and robustness to modeling assumptions. The results show differences between the methods in terms of the quantities being estimated and in efficiency. We also demonstrate how the results of a randomized trial of salvage hormone therapy are strongly influenced by the design of the study and discuss how the findings from using the three methodologies can be used to infer the results of a trial.
authors.affiliation: Department of Biostatistics, University of Michigan, Ann Arbor, MI, U.S.A.
authors.forename: Jeremy M G
authors.initials: JM
authors.lastname: Taylor
authors.forename: Jincheng
authors.initials: J
authors.lastname: Shen
authors.forename: Edward H
authors.initials: EH
authors.lastname: Kennedy
authors.forename: Lu
authors.initials: L
authors.lastname: Wang
authors.forename: Douglas E
authors.initials: DE
authors.lastname: Schaubel
mesh_terms: D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D006801:Humans; D008297:Male; D015233:Models, Statistical; D009364:Neoplasm Recurrence, Local; D017430:Prostate-Specific Antigen / Q000097:blood*; D011471:Prostatic Neoplasms / Q000188:drug therapy / Q000473:pathology*; D016032:Randomized Controlled Trials as Topic / Q000379:methods*; D016879:Salvage Therapy / Q000379:methods* / Q000592:standards; D016896:Treatment Outcome
chemical_list: D017430:Prostate-Specific Antigen
keywords: causal effect; proportional hazards model; prostate cancer; time-dependent confounder; treatment by indication